Plant Biology-Inspired Genetic Algorithm: Superior Efficiency to Firefly Optimizer

  • Neeraj Gupta
  • Mahdi KhosravyEmail author
  • Om Prakash Mahela
  • Nilesh Patel
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)


This chapter analytically compares the efficiency of the recent plant biology-inspired genetic algorithm (PBGA) and the firefly algorithm (FA) optimizer. The comparison is over a range of well-known critical benchmark test functions. Through statistical comparisons over the benchmark functions, the efficiency of PBGA has been evaluated versus FA as a well-known accurate meta-heuristic optimizer. Through a considerable number of Monte Carlo runs of searching for a solution by both optimizers, their performance has been statistically measured by several valid indices. In addition, the convergence curves give a visual comparison of both techniques where the stability, speed, and accuracy dominance of PBGA is clearly observable. However, in the case of benchmark function with smooth nature-like Rosenbrock, Sphere, and Dixon and Price, FA has better performance on average, while PBGA performance is still comparable to FA.



Our very special acknowledgment goes to Professor Ishwar Sethi in the Department of Computer Science and Engineering, Oakland University, Rochester, Michigan, the USA, for his very worthwhile advices during this work.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Neeraj Gupta
    • 1
  • Mahdi Khosravy
    • 2
    • 3
    Email author
  • Om Prakash Mahela
    • 4
  • Nilesh Patel
    • 1
  1. 1.Department of Computer Science and EngineeringOakland UniversityRochester, OaklandUSA
  2. 2.Electrical Engineering DepartmentFederal University of Juiz de ForaJuiz de ForaBrazil
  3. 3.Electrical Engineering DepartmentUniversity of the RyukyusNishiharaJapan
  4. 4.Rajasthan Rajya Vdhyut Prasaran Nigam Ltd.JodhpurIndia

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